摘要
针对同一空域内多无人机飞行冲突解脱问题,提出了一种基于改进人工蜂群算法的冲突解脱策略。在传统蜂群算法的基础上改进了跟随蜂对雇佣峰的选择概率及跟随蜂的搜索策略,发挥了迭代过程中最优解的引导作用,保持了传统人工蜂群算法全局搜索和跳出局部最优的能力,解决了传统人工蜂群算法局部搜索效率较低的问题,提升了收敛性能,增加了得到最优解的概率。利用该算法通过航向调整和速度调整2种策略实现了多机的冲突解脱。对比仿真结果验证:该方法在收敛速度、运行速度和最优解的适应度等方面都较遗传算法有很大提升。
Aiming at the problem of multiple UAVs confliction resolution,a scheme based on modified artifi-cial bee colony （MABC）algorithm was proposed.On the basis of the artificial bee colony （ABC）algo-rithm,the probability function and searching mechanism for the onlooker bees are modified to make the best solution play the role of guidance to enhance the exploitation capability and improve the convergence performance.The abilities for exploration and j umping out from local optimum are kept.Both of heading change resolution and speed change resolution are utilized to solve the confliction.The results demonstrate good performance for the convergence speed,running speed and the fitness value of best solution when compared with genetic algorithm.
出处
《空军工程大学学报（自然科学版）》
CSCD
北大核心
2014年第3期10-14,共5页
Journal of Air Force Engineering University(Natural Science Edition)
基金
航空科学基金资助项目(20121396008)
关键词
人工蜂群算法
多机飞行冲突
冲突解脱
收敛性能
artificial bee colony algorithm
multi-vehicle confliction
confliction resolution
convergence performance
